Improving Rumor Detection Performance by Using Bias Attributes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A5C7PYXB4" target="_blank" >RIV/00216208:11320/26:5C7PYXB4 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TCSS.2025.3550170" target="_blank" >http://dx.doi.org/10.1109/TCSS.2025.3550170</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TCSS.2025.3550170" target="_blank" >10.1109/TCSS.2025.3550170</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Rumor Detection Performance by Using Bias Attributes

Popis výsledku v původním jazyce

With the rapid advancement of social media, the barriers to sharing information have significantly decreased, leading to the rampant spread of rumors across various platforms. Current natural language processing (NLP) techniques use models trained on datasets to detect rumors. However, these datasets often contain inherent biases, which, as has been demonstrated in other NLP tasks, can negatively impact the accuracy of the results. This study confirms the presence of significant bias in rumor detection datasets. Given the intertwined and complex nature of bias and rumors, both of which play a crucial role in assessing the trustworthiness of online content, the article argues that improving detection models should not only focus on performance but also prioritize detecting biased rumors. Addressing this dual challenge is essential to prevent rumor creators from exploiting biases to enhance the spread of false information. To tackle this issue at the data level, this study proposes integrating bias attributes into the training datasets, thereby improving the ability of models to identify biased rumors. Through experiments conducted across multiple rumor detection models, the approach has been shown to enhance the detection of biased rumors in both Chinese and English datasets without compromising overall detection accuracy. This method might be helpful in reducing the spread of biased rumors online, contributing to a healthier information ecosystem. © 2014 IEEE.

Název v anglickém jazyce

Improving Rumor Detection Performance by Using Bias Attributes

Popis výsledku anglicky

With the rapid advancement of social media, the barriers to sharing information have significantly decreased, leading to the rampant spread of rumors across various platforms. Current natural language processing (NLP) techniques use models trained on datasets to detect rumors. However, these datasets often contain inherent biases, which, as has been demonstrated in other NLP tasks, can negatively impact the accuracy of the results. This study confirms the presence of significant bias in rumor detection datasets. Given the intertwined and complex nature of bias and rumors, both of which play a crucial role in assessing the trustworthiness of online content, the article argues that improving detection models should not only focus on performance but also prioritize detecting biased rumors. Addressing this dual challenge is essential to prevent rumor creators from exploiting biases to enhance the spread of false information. To tackle this issue at the data level, this study proposes integrating bias attributes into the training datasets, thereby improving the ability of models to identify biased rumors. Through experiments conducted across multiple rumor detection models, the approach has been shown to enhance the detection of biased rumors in both Chinese and English datasets without compromising overall detection accuracy. This method might be helpful in reducing the spread of biased rumors online, contributing to a healthier information ecosystem. © 2014 IEEE.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Computational Social Systems

ISSN

2329-924X

e-ISSN

—

Svazek periodika

2025

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105001234098